Sperm mobility is predictive of the relative genetic contribution among competing mating geese, as determined by microsatellite genotype identification of potential sires
Bibliographic record
Abstract
The low reproductive efficiency (RE) of geese limits their production in the poultry industry. To select ganders with high breeding potential, the effect of 3 sperm mobility ranks (SMRs; high-, medium-, and low-SMR) on the RE of naturally mating geese was determined. To exclude the confounding effect of social rank (SR) on RE in naturally mating flocks, a 2-factor nested experimental design was used to differentiate the effects of SMR and SR on RE. Twenty-seven ganders and 135 geese (Zi geese, Anser cygnoides L.) at approximately 1 yr of age were divided into 3 flocks, each of which included the 3 SMR groups. Each SMR group included 3 ganders and 15 female geese. Relative genetic contribution (RGC) is defined as the number of offspring sired by 1 male as a percentage of the entire goslings in each flock, and it was used to compare the differences in RE among ganders. The frequency of agonistic behavioral interactions (ABIs) among the ganders was video recorded in each SMR group, and the SR of each gander was determined. In total, 1,026 eggs were incubated, and 609 goslings hatched. Parent-offspring relationships among 771 individuals from the 2 generations were identified using 20 microsatellite markers, and the RGC was calculated. Results showed that the SMR and SR had significant effects on RGC in naturally mating geese (P = 0.001 and P = 0.000, respectively). Significant differences in RGC were observed among the high- and medium- and low-SMR groups, with average RGCs of 14.3, 10.6, and 8.4%, respectively. The high-SMR group had the highest RGCs in each flock, and the ganders with high SR had the highest RGCs among the 3 SMRs. The study showed that in a naturally mating geese population, selecting for the sperm mobility traits of a gander can effectively improve the RE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".